ASR is now present on a wide variety of platforms, ranging from huge server farms to small embedded devices. Each platform has its own resource limitations and this variety of requirements makes building acoustic models (AMs) for all these platforms a delicate task of balancing compromises between model size, decoding speed, and accuracy. In practice, AMs are custom built for each of these platforms. Furthermore, in ASR, it is often desirable to dynamically adjust the complexity of models depending on the load on the machine on which recognition is being carried out. In times of heavy use, it is desirable to use models of low complexity so that a larger number of engines can be run. On the other hand, when the resources are available, it is feasible to use more complex and more accurate models. Current techniques rely on building and storing multiple models of varying complexity. This causes an overhead in model development time. It also results in storage cost at run time. Furthermore, we are limited in complexity control by the number of models available.